{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75e2ef28-594f-4c18-9d22-c6b8cd40ead2",
   "metadata": {},
   "source": [
    "# Day 3 - Conversational AI - aka Chatbot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70e39cd8-ec79-4e3e-9c26-5659d42d0861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "231605aa-fccb-447e-89cf-8b187444536a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables in a file called .env\n",
    "# Print the key prefixes to help with any debugging\n",
    "\n",
    "load_dotenv(override=True)\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6541d58e-2297-4de1-b1f7-77da1b98b8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize\n",
    "\n",
    "openai = OpenAI()\n",
    "MODEL = 'gpt-4.1-mini'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e16839b5-c03b-4d9d-add6-87a0f6f37575",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again, I'll be in scientist-mode and change this global during the lab\n",
    "\n",
    "system_message = \"You are a helpful assistant\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e97227-f162-4d1a-a0b2-345ff248cbe7",
   "metadata": {},
   "source": [
    "## And now, writing a new callback\n",
    "\n",
    "We now need to write a function called:\n",
    "\n",
    "`chat(message, history)`\n",
    "\n",
    "Which will be a callback function we will give gradio.\n",
    "\n",
    "### The job of this function\n",
    "\n",
    "Take a message, take the prior conversation, and return the response.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "354ce793",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    return \"bananas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e87f3417",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d4996e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    return f\"You said {message} and the history is {history} but I still say bananas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434a0417",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7890cac3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f7330d7f",
   "metadata": {},
   "source": [
    "## OK! Let's write a slightly better chat callback!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eacc8a4-4b48-4358-9e06-ce0020041bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def chat(message, history):\n",
    "    history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
    "    messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    response = openai.chat.completions.create(model=MODEL, messages=messages)\n",
    "    return response.choices[0].message.content\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ab706f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bce145a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
    "    messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
    "    response = \"\"\n",
    "    for chunk in stream:\n",
    "        response += chunk.choices[0].delta.content or ''\n",
    "        yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8beeca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1334422a-808f-4147-9c4c-57d63d9780d0",
   "metadata": {},
   "source": [
    "## OK let's keep going!\n",
    "\n",
    "Using a system message to add context, and to give an example answer.. this is \"one shot prompting\" again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f91b414-8bab-472d-b9c9-3fa51259bdfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are a helpful assistant in a clothes store. You should try to gently encourage \\\n",
    "the customer to try items that are on sale. Hats are 60% off, and most other items are 50% off. \\\n",
    "For example, if the customer says 'I'm looking to buy a hat', \\\n",
    "you could reply something like, 'Wonderful - we have lots of hats - including several that are part of our sales event.'\\\n",
    "Encourage the customer to buy hats if they are unsure what to get.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "413e9e4e-7836-43ac-a0c3-e1ab5ed6b136",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75f0ffa-55c8-4152-b451-945021676837",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message += \"\\nIf the customer asks for shoes, you should respond that shoes are not on sale today, \\\n",
    "but remind the customer to look at hats!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c602a8dd-2df7-4eb7-b539-4e01865a6351",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a987a66-1061-46d6-a83a-a30859dc88bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def chat(message, history):\n",
    "    history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
    "    relevant_system_message = system_message\n",
    "    if 'belt' in message.lower():\n",
    "        relevant_system_message += \" The store does not sell belts; if you are asked for belts, be sure to point out other items on sale.\"\n",
    "    \n",
    "    messages = [{\"role\": \"system\", \"content\": relevant_system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "\n",
    "    stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n",
    "\n",
    "    response = \"\"\n",
    "    for chunk in stream:\n",
    "        response += chunk.choices[0].delta.content or ''\n",
    "        yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20570de2-eaad-42cc-a92c-c779d71b48b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a57ee0-b945-48a7-a024-01b56a5d4b3e",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#181;\">Business Applications</h2>\n",
    "            <span style=\"color:#181;\">Conversational Assistants are of course a hugely common use case for Gen AI, and the latest frontier models are remarkably good at nuanced conversation. And Gradio makes it easy to have a user interface. Another crucial skill we covered is how to use prompting to provide context, information and examples.\n",
    "<br/><br/>\n",
    "Consider how you could apply an AI Assistant to your business, and make yourself a prototype. Use the system prompt to give context on your business, and set the tone for the LLM.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc0e5a9",
   "metadata": {},
   "source": []
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